Tag Archives: geometry

Physics of a singing saw could lead to applications in sensing, nanoelectronics, photonics, etc.

I’d forgotten how haunting a musical saw can sound,

An April 22, 2022 news item on Nanowerk announces research into the possibilities of a singing saw,

The eerie, ethereal sound of the singing saw has been a part of folk music traditions around the globe, from China to Appalachia, since the proliferation of cheap, flexible steel in the early 19th century. Made from bending a metal hand saw and bowing it like a cello, the instrument reached its heyday on the vaudeville stages of the early 20th century and has seen a resurgence thanks, in part, to social media.

As it turns out, the unique mathematical physics of the singing saw may hold the key to designing high quality resonators for a range of applications.

In a new paper, a team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Department of Physics used the singing saw to demonstrate how the geometry of a curved sheet, like curved metal, could be tuned to create high-quality, long-lasting oscillations for applications in sensing, nanoelectronics, photonics and more.

An April 21, 2022 Harvard University John A. Paulson School of Engineering and Applied Sciences (SEAS) news release by Leah Burrows (also on EurekAlert but published on April 22, 2022) delves further into physics of singing saws,

“Our research offers a robust principle to design high-quality resonators independent of scale and material, from macroscopic musical instruments to nanoscale devices, simply through a combination of geometry and topology,” said L Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, of Organismic and Evolutionary Biology, and of Physics and senior author of the study.

While all musical instruments are acoustic resonators of a kind, none work quite like the singing saw.

“How the singing saw sings is based on a surprising effect,” said Petur Bryde, a graduate student at SEAS and co-first author of the paper. “When you strike a flat elastic sheet, such as a sheet of metal, the entire structure vibrates. The energy is quickly lost through the boundary where it is held, resulting in a dull sound that dissipates quickly. The same result is observed if you curve it into a J-shape. But, if you bend the sheet into an S-shape, you can make it vibrate in a very small area, which produces a clear, long-lasting tone.”

The geometry of the curved saw creates what musicians call the sweet spot and what physicists call localized vibrational modes — a confined area on the sheet which resonates without losing energy at the edges.

Importantly, the specific geometry of the S-curve doesn’t matter. It could be an S with a big curve at the top and a small curve at the bottom or visa versa. 

“Musicians and researchers have known about this robust effect of geometry for some time, but the underlying mechanisms have remained a mystery,” said Suraj Shankar, a Harvard Junior Fellow in Physics and SEAS and co-first author of the study.  “We found a mathematical argument that explains how and why this robust effect exists with any shape within this class, so that the details of the shape are unimportant, and the only fact that matters is that there is a reversal of curvature along the saw.”

Shankar, Bryde and Mahadevan found that explanation via an analogy to very different class of physical systems — topological insulators. Most often associated with quantum physics, topological insulators are materials that conduct electricity in their surface or edge but not in the middle and no matter how you cut these materials, they will always conduct on their edges.

“In this work, we drew a mathematical analogy between the acoustics of bent sheets and these quantum and electronic systems,” said Shankar.

By using the mathematics of topological systems, the researchers found that the localized vibrational modes in the sweet spot of singing saw were governed by a topological parameter that can be computed and which relies on nothing more than the existence of two opposite curves in the material. The sweet spot then behaves like an internal “edge” in the saw.

“By using experiments, theoretical and numerical analysis, we showed that the S-curvature in a thin shell can localize topologically-protected modes at the ‘sweet spot’ or inflection line, similar to exotic edge states in topological insulators,” said Bryde. “This phenomenon is material independent, meaning it will appear in steel, glass or even graphene.”

The researchers also found that they could tune the localization of the mode by changing the shape of the S-curve, which is important in applications such as sensing, where you need a resonator that is tuned to very specific frequencies.

Next, the researchers aim to explore localized modes in doubly curved structures, such as bells and other shapes.

Here’s a link to and a citation for the paper,

Geometric control of topological dynamics in a singing saw by Suraj Shankar, Petur Bryde, and L. Mahadevan. The Proceedings of the National Academy of Sciences (PNAS) April 21, 2022 | 119 (17) e2117241119 DOI: https://doi.org/10.1073/pnas.2117241119

This paper is open (free) access.

Fractal brain structures and story listening

For anyone who needs to brush up on their fractals,

Caption: Zoomed in detail of the Mandelbrot set, a famous fractal, at different spatial scales of 1x, 4x, 16x, and 64x (from left to right). Credit: Image by Jeremy R. Manning.

My September 3, 2012 posting (Islands of Benoît Mandelbrot: Fractals, Chaos, and the Materiality of Thinking exhibition opening in Sept. 2012 in New York) includes an explanation of fractals. There is another explanation in the news release that follows below.

The story

A September 30, 2021 Dartmouth College news release announces work from a team of researchers using the concept of fractals as a way of understanding how the brain works (Note: Links have been removed),

Understanding how the human brain produces complex thought is daunting given its intricacy and scale. The brain contains approximately 100 billion neurons that coordinate activity through 100 trillion connections, and those connections are organized into networks that are often similar from one person to the next. A Dartmouth study has found a new way to look at brain networks using the mathematical notion of fractals, to convey communication patterns between different brain regions as people listened to a short story.The results are published in Nature Communications.

“To generate our thoughts, our brains create this amazing lightning storm of connection patterns,” said senior author Jeremy R. Manning, an assistant professor of psychological and brain sciences, and director of the Contextual Dynamics Lab at Dartmouth. “The patterns look beautiful, but they are also incredibly complicated. Our mathematical framework lets us quantify how those patterns relate at different scales, and how they change over time.”

In the field of geometry, fractals are shapes that appear similar at different scales. Within a fractal, shapes and patterns are repeated in an infinite cascade, such as spirals comprised of smaller spirals that are in turn comprised of still-smaller spirals, and so on. Dartmouth’s study shows that brain networks organize in a similar way: patterns of brain interactions are mirrored simultaneously at different scales. When people engage in complex thoughts, their networks seem to spontaneously organize into fractal-like patterns. When those thoughts are disrupted, the fractal patterns become scrambled and lose their integrity.

The researchers developed a mathematical framework that identifies similarities in network interactions at different scales or “orders.” When brain structures do not exhibit any consistent patterns of interaction, the team referred to this as a “zero-order” pattern. When individual pairs of brain structures interact, this is called a “first-order” pattern. “Second-order” patterns refer to similar patterns of interactions in different sets of brain structures, at different scales. When patterns of interaction become fractal— “first-order” or higher— the order denotes the number of times the patterns are repeated at different scales.

The study shows that when people listened to an audio recording of a 10-minute story, their brain networks spontaneously organized into fourth-order network patterns. However, this organization was disrupted when people listened to altered versions of the recording. For example, when the story’s paragraphs were randomly shuffled, preserving some but not all of the story’s meaning, people’s brain networks displayed only second-order patterns. When every word of the story was shuffled, this disrupted all but the lowest level (zero-order) patterns.

“The more finely the story was shuffled, the more the fractal structures of the network patterns were disrupted,” said first author Lucy Owen, a graduate student in psychological and brain sciences at Dartmouth. “Since the disruptions in those fractal patterns seemed directly linked with how well people could make sense of the story, this finding may provide clues about how our brain structures work together to understand what is happening in the narrative.”

The fractal network patterns were surprisingly similar across people: patterns from one group could be used to accurately estimate what part of the story another group was listening to.

The team also studied which brain structures were interacting to produce these fractal patterns. The results show that the smallest scale (first-order) interactions occurred in brain regions that process raw sounds. Second-order interactions linked these raw sounds with speech processing regions, and third-order interactions linked sound and speech areas with a network of visual processing regions. The largest-scale (fourth-order) interactions linked these auditory and visual sensory networks with brain structures that support high-level thinking. According to the researchers, when these networks organize at multiple scales, this may show how the brain processes raw sensory information into complex thought—from raw sounds, to speech, to visualization, to full-on understanding.

The researchers’ computational framework can also be applied to areas beyond neuroscience and the team has already begun using an analogous approach to explore interactions in stock prices and animal migration patterns.

Here’s a link to and a citation for the paper,

High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns by Lucy L. W. Owen, Thomas H. Chang & Jeremy R. Manning. Nature Communications volume 12, Article number: 5728 (2021) DOI: https://doi.org/10.1038/s41467-021-25876-x Published: 30 September 2021

This paper is open access.

“The earth is mostly made of cubes,” said Plato in 5th Century BCE. Turns out, he was right!

Theories from mathematics, physics, and geology have been used to demonstrate that the earth’s basic shape is, roughly speaking, a cube. From a July 20, 2020 news item on ScienceDaily,

Plato, the Greek philosopher who lived in the 5th century B.C.E. [before the common era], believed that the universe was made of five types of matter: earth, air, fire, water, and cosmos. Each was described with a particular geometry, a platonic shape. For earth, that shape was the cube.

Science has steadily moved beyond Plato’s conjectures, looking instead to the atom as the building block of the universe. Yet Plato seems to have been onto something, researchers have found.

In a new paper in the Proceedings of the National Academy of Sciences [PNAS], a team from the University of Pennsylvania, Budapest University of Technology and Economics, and University of Debrecen [Hungary] uses math, geology, and physics to demonstrate that the average shape of rocks on Earth is a cube.

A July 17, 2020 University of Pennsylvania news release (also on EurekAlert but dated July 20, 2020), which originated the news item, goes on to describe the work as “mind-blowing,”

“Plato is widely recognized as the first person to develop the concept of an atom [Maybe not, scroll down to find the subhead “Leucippus and Democritus”], the idea that matter is composed of some indivisible component at the smallest scale,” says Douglas Jerolmack, a geophysicist in Penn’s School of Arts & Sciences’ Department of Earth and Environmental Science and the School of Engineering and Applied Science’s Department of Mechanical Engineering and Applied Mechanics. “But that understanding was only conceptual; nothing about our modern understanding of atoms derives from what Plato told us.

“The interesting thing here is that what we find with rock, or earth, is that there is more than a conceptual lineage back to Plato. It turns out that Plato’s conception about the element earth being made up of cubes is, literally, the statistical average model for real earth. And that is just mind-blowing.”

The group’s finding began with geometric models developed by mathematician Gábor Domokos of the Budapest University of Technology and Economics, whose work predicted that natural rocks would fragment into cubic shapes.

“This paper is the result of three years of serious thinking and work, but it comes back to one core idea,” says Domokos. “If you take a three-dimensional polyhedral shape, slice it randomly into two fragments and then slice these fragments again and again, you get a vast number of different polyhedral shapes. But in an average sense, the resulting shape of the fragments is a cube.”

Domokos pulled two Hungarian theoretical physicists into the loop: Ferenc Kun, an expert on fragmentation, and János Török, an expert on statistical and computational models. After discussing the potential of the discovery, Jerolmack says, the Hungarian researchers took their finding to Jerolmack to work together on the geophysical questions; in other words, “How does nature let this happen?”

“When we took this to Doug, he said, ‘This is either a mistake, or this is big,'” Domokos recalls. “We worked backward to understand the physics that results in these shapes.”

Fundamentally, the question they answered is what shapes are created when rocks break into pieces. Remarkably, they found that the core mathematical conjecture unites geological processes not only on Earth but around the solar system as well.

“Fragmentation is this ubiquitous process that is grinding down planetary materials,” Jerolmack says. “The solar system is littered with ice and rocks that are ceaselessly smashing apart. This work gives us a signature of that process that we’ve never seen before.”

Part of this understanding is that the components that break out of a formerly solid object must fit together without any gaps, like a dropped dish on the verge of breaking. As it turns out, the only one of the so-called platonic forms–polyhedra with sides of equal length–that fit together without gaps are cubes.

“One thing we’ve speculated in our group is that, quite possibly Plato looked at a rock outcrop and after processing or analyzing the image subconsciously in his mind, he conjectured that the average shape is something like a cube,” Jerolmack says.

“Plato was very sensitive to geometry,” Domokos adds. According to lore, the phrase “Let no one ignorant of geometry enter” was engraved at the door to Plato’s Academy. “His intuitions, backed by his broad thinking about science, may have led him to this idea about cubes,” says Domokos.

To test whether their mathematical models held true in nature, the team measured a wide variety of rocks, hundreds that they collected and thousands more from previously collected datasets. No matter whether the rocks had naturally weathered from a large outcropping or been dynamited out by humans, the team found a good fit to the cubic average.

However, special rock formations exist that appear to break the cubic “rule.” The Giant’s Causeway in Northern Ireland, with its soaring vertical columns, is one example, formed by the unusual process of cooling basalt. These formations, though rare, are still encompassed by the team’s mathematical conception of fragmentation; they are just explained by out-of-the-ordinary processes at work.

“The world is a messy place,” says Jerolmack. “Nine times out of 10, if a rock gets pulled apart or squeezed or sheared–and usually these forces are happening together–you end up with fragments which are, on average, cubic shapes. It’s only if you have a very special stress condition that you get something else. The earth just doesn’t do this often.”

The researchers also explored fragmentation in two dimensions, or on thin surfaces that function as two-dimensional shapes, with a depth that is significantly smaller than the width and length. There, the fracture patterns are different, though the central concept of splitting polygons and arriving at predictable average shapes still holds.

“It turns out in two dimensions you’re about equally likely to get either a rectangle or a hexagon in nature,” Jerolmack says. “They’re not true hexagons, but they’re the statistical equivalent in a geometric sense. You can think of it like paint cracking; a force is acting to pull the paint apart equally from different sides, creating a hexagonal shape when it cracks.”

In nature, examples of these two-dimensional fracture patterns can be found in ice sheets, drying mud, or even the earth’s crust, the depth of which is far outstripped by its lateral extent, allowing it to function as a de facto two-dimensional material. It was previously known that the earth’s crust fractured in this way, but the group’s observations support the idea that the fragmentation pattern results from plate tectonics.

Identifying these patterns in rock may help in predicting phenomenon such as rock fall hazards or the likelihood and location of fluid flows, such as oil or water, in rocks.

For the researchers, finding what appears to be a fundamental rule of nature emerging from millennia-old insights has been an intense but satisfying experience.

“There are a lot of sand grains, pebbles, and asteroids out there, and all of them evolve by chipping in a universal manner,” says Domokos, who is also co-inventor of the Gömböc, the first known convex shape with the minimal number–just two–of static balance points. Chipping by collisions gradually eliminates balance points, but shapes stop short of becoming a Gömböc; the latter appears as an unattainable end point of this natural process.

The current result shows that the starting point may be a similarly iconic geometric shape: the cube with its 26 balance points. “The fact that pure geometry provides these brackets for a ubiquitous natural process, gives me happiness,” he says.

“When you pick up a rock in nature, it’s not a perfect cube, but each one is a kind of statistical shadow of a cube,” adds Jerolmack. “It calls to mind Plato’s allegory of the cave. He posited an idealized form that was essential for understanding the universe, but all we see are distorted shadows of that perfect form.”

The human capacity for imagination, in this case linking ideas about geometry and mathematics from the 5th Century BCE to modern physics and geology and to the solar system, astounds and astonishes me. As for Jerolmack’s comment that Plato (428/427 or 424/423 – 348/347 BC) was the first to develop the concept of an atom, not everyone agrees.

Leucippus and Democritus

It may not ever be possible to determine who was the first to theorize/philosophize about atoms but there is relatively general agreement that Leucippus (5th cent.BCE) and his successor, Democritus (c. 460 – c. 370 BC) were among the first. Here’s more about Ancient Atomism and its origins from the Stanford Encyclopedia of Philosphy,

Leucippus (5th c. BCE) is the earliest figure whose commitment to atomism is well attested. He is usually credited with inventing atomism. According to a passing remark by the geographer Strabo, Posidonius (1st c. BCE Stoic philosopher) reported that ancient Greek atomism can be traced back to a figure known as Moschus or Mochus of Sidon, who lived at the time of the Trojan wars. This report was given credence in the seventeenth century: the Cambridge Platonist Henry More traced the origins of ancient atomism back, via Pythagoras and Moschus, to Moses. This theologically motivated view does not seem to claim much historical evidence, however.

Leucippus and Democritus are widely regarded as the first atomists [emphasis mine] in the Greek tradition. Little is known about Leucippus, while the ideas of his student Democritus—who is said to have taken over and systematized his teacher’s theory—are known from a large number of reports. These ancient atomists theorized that the two fundamental and oppositely characterized constituents of the natural world are indivisible bodies—atoms—and void. The latter is described simply as nothing, or the negation of body. Atoms are by their nature intrinsically unchangeable; they can only move about in the void and combine into different clusters. Since the atoms are separated by void, they cannot fuse, but must rather bounce off one another when they collide. Because all macroscopic objects are in fact combinations of atoms, everything in the macroscopic world is subject to change, as their constituent atoms shift or move away. Thus, while the atoms themselves persist through all time, everything in the world of our experience is transitory and subject to dissolution.

Although the Greek term atomos is most commonly associated with the philosophical system developed by Leucippus and Democritus, involving solid and impenetrable bodies, Plato’s [emphasis mine] Timaeus presents a different kind of physical theory based on indivisibles. The dialogue elaborates an account of the world wherein the four different basic kinds of matter—earth, air, fire, and water—are regular solids composed from plane figures: isoceles and scalene right-angled triangles. Because the same triangles can form into different regular solids, the theory thus explains how some of the elements can transform into one another, as was widely believed.

As you can see from the excerpt, they are guessing as to the source for atomism and thee are different kinds of atomism and Plato staked his own atomistic territory.

The paper

Here’s a link to and a citation for the paper followed by a statement of significance and the paper’s abstract,

Plato’s cube and the natural geometry of fragmentation by Gábor Domokos, Douglas J. Jerolmack, Ferenc Kun, and János Török. PNAS DOI: https://doi.org/10.1073/pnas.2001037117 First published July 17, 2020

This paper is behind a paywall.

Now for the Significance and the Abstract,

We live on and among the by-products of fragmentation, from nanoparticles to rock falls to glaciers to continents. Understanding and taming fragmentation is central to assessing natural hazards and extracting resources, and even for landing probes safely on other planetary bodies. In this study, we draw inspiration from an unlikely and ancient source: Plato, who proposed that the element Earth is made of cubes because they may be tightly packed together. We demonstrate that this idea is essentially correct: Appropriately averaged properties of most natural 3D fragments reproduce the topological cube. We use mechanical and geometric models to explain the ubiquity of Plato’s cube in fragmentation and to uniquely map distinct fragment patterns to their formative stress conditions.

Plato envisioned Earth’s building blocks as cubes, a shape rarely found in nature. The solar system is littered, however, with distorted polyhedra—shards of rock and ice produced by ubiquitous fragmentation. We apply the theory of convex mosaics to show that the average geometry of natural two-dimensional (2D) fragments, from mud cracks to Earth’s tectonic plates, has two attractors: “Platonic” quadrangles and “Voronoi” hexagons. In three dimensions (3D), the Platonic attractor is dominant: Remarkably, the average shape of natural rock fragments is cuboid. When viewed through the lens of convex mosaics, natural fragments are indeed geometric shadows of Plato’s forms. Simulations show that generic binary breakup drives all mosaics toward the Platonic attractor, explaining the ubiquity of cuboid averages. Deviations from binary fracture produce more exotic patterns that are genetically linked to the formative stress field. We compute the universal pattern generator establishing this link, for 2D and 3D fragmentation.

Fascinating, eh?

Geometry and art, an exhibition in Toronto (Canada)

I received this notice from ArtSci Salon mailing (on February 7, 2020 via email),

Geometry is Life

Robin Kingsburgh

February 5 — 16, 2020
Opening Reception: Saturday, February 8, 2 — 5 pm​

Cicada (detail), Robin Kingsburgh (Acrylic on MDF board, 36″ x 38″, 2018)

My work takes inspiration from geometry. For me the square and the circle are starting points. And ending points. The square, defined by the horizontal and the vertical: it’s all you need. The circle: a snake biting its tail; the beginning and end; the still point. Geometric archetypes. But there is no perfect circle; there is no perfect square. The beauty of Pythagoras is within our minds. Rendered by the human hand, the square becomes imperfect, and becomes a part of the human world – where imperfection reigns. The rhythm of imperfection is beauty, where order and chaos dance, and sometimes balance.

Robin Kingsburgh is a trained astronomer (Ph.D. in Astronomy, 1992, University College London). Her artistic education comes from studies at University of Toronto, as well as in the U.K. and France, and has paralleled her scientific development. She currently teaches various Natural Science courses at York University, Toronto. Her scientific background influences her artwork in an indirect, subconscious way, where she employs geometric motifs as a frequent theme. She is a member of Propeller Gallery, where she shows her artwork on a regular basis. She has recently been elected to the Ontario Society of Artists.

There you have it. Have a nice weekend!

ETA February 10, 2020: I’m sorry I forgot to include the address: Propeller Gallery, 30 Abell St Toronto. Wed-Sat 12-6pm, Sun 12-5pm

Fusing graphene flakes for 3D graphene structures that are 10x as strong as steel

A Jan. 6, 2017 news item on Nanowerk describes how geometry may have as much or more to do with the strength of 3D graphene structures than the graphene used to create them,

A team of researchers at MIT [Massachusetts Institute of Technology] has designed one of the strongest lightweight materials known, by compressing and fusing flakes of graphene, a two-dimensional form of carbon. The new material, a sponge-like configuration with a density of just 5 percent, can have a strength 10 times that of steel.

In its two-dimensional form, graphene is thought to be the strongest of all known materials. But researchers until now have had a hard time translating that two-dimensional strength into useful three-dimensional materials.

The new findings show that the crucial aspect of the new 3-D forms has more to do with their unusual geometrical configuration than with the material itself, which suggests that similar strong, lightweight materials could be made from a variety of materials by creating similar geometric features.

The findings are being reported today [Jan. 6, 2017\ in the journal Science Advances, in a paper by Markus Buehler, the head of MIT’s Department of Civil and Environmental Engineering (CEE) and the McAfee Professor of Engineering; Zhao Qin, a CEE research scientist; Gang Seob Jung, a graduate student; and Min Jeong Kang MEng ’16, a recent graduate.

A Jan. 6, 2017 MIT news release (also on EurekAlert), which originated the news item, describes the research in more detail,

Other groups had suggested the possibility of such lightweight structures, but lab experiments so far had failed to match predictions, with some results exhibiting several orders of magnitude less strength than expected. The MIT team decided to solve the mystery by analyzing the material’s behavior down to the level of individual atoms within the structure. They were able to produce a mathematical framework that very closely matches experimental observations.

Two-dimensional materials — basically flat sheets that are just one atom in thickness but can be indefinitely large in the other dimensions — have exceptional strength as well as unique electrical properties. But because of their extraordinary thinness, “they are not very useful for making 3-D materials that could be used in vehicles, buildings, or devices,” Buehler says. “What we’ve done is to realize the wish of translating these 2-D materials into three-dimensional structures.”

The team was able to compress small flakes of graphene using a combination of heat and pressure. This process produced a strong, stable structure whose form resembles that of some corals and microscopic creatures called diatoms. These shapes, which have an enormous surface area in proportion to their volume, proved to be remarkably strong. “Once we created these 3-D structures, we wanted to see what’s the limit — what’s the strongest possible material we can produce,” says Qin. To do that, they created a variety of 3-D models and then subjected them to various tests. In computational simulations, which mimic the loading conditions in the tensile and compression tests performed in a tensile loading machine, “one of our samples has 5 percent the density of steel, but 10 times the strength,” Qin says.

Buehler says that what happens to their 3-D graphene material, which is composed of curved surfaces under deformation, resembles what would happen with sheets of paper. Paper has little strength along its length and width, and can be easily crumpled up. But when made into certain shapes, for example rolled into a tube, suddenly the strength along the length of the tube is much greater and can support substantial weight. Similarly, the geometric arrangement of the graphene flakes after treatment naturally forms a very strong configuration.

The new configurations have been made in the lab using a high-resolution, multimaterial 3-D printer. They were mechanically tested for their tensile and compressive properties, and their mechanical response under loading was simulated using the team’s theoretical models. The results from the experiments and simulations matched accurately.

The new, more accurate results, based on atomistic computational modeling by the MIT team, ruled out a possibility proposed previously by other teams: that it might be possible to make 3-D graphene structures so lightweight that they would actually be lighter than air, and could be used as a durable replacement for helium in balloons. The current work shows, however, that at such low densities, the material would not have sufficient strength and would collapse from the surrounding air pressure.

But many other possible applications of the material could eventually be feasible, the researchers say, for uses that require a combination of extreme strength and light weight. “You could either use the real graphene material or use the geometry we discovered with other materials, like polymers or metals,” Buehler says, to gain similar advantages of strength combined with advantages in cost, processing methods, or other material properties (such as transparency or electrical conductivity).

“You can replace the material itself with anything,” Buehler says. “The geometry is the dominant factor. It’s something that has the potential to transfer to many things.”

The unusual geometric shapes that graphene naturally forms under heat and pressure look something like a Nerf ball — round, but full of holes. These shapes, known as gyroids, are so complex that “actually making them using conventional manufacturing methods is probably impossible,” Buehler says. The team used 3-D-printed models of the structure, enlarged to thousands of times their natural size, for testing purposes.

For actual synthesis, the researchers say, one possibility is to use the polymer or metal particles as templates, coat them with graphene by chemical vapor deposit before heat and pressure treatments, and then chemically or physically remove the polymer or metal phases to leave 3-D graphene in the gyroid form. For this, the computational model given in the current study provides a guideline to evaluate the mechanical quality of the synthesis output.

The same geometry could even be applied to large-scale structural materials, they suggest. For example, concrete for a structure such a bridge might be made with this porous geometry, providing comparable strength with a fraction of the weight. This approach would have the additional benefit of providing good insulation because of the large amount of enclosed airspace within it.

Because the shape is riddled with very tiny pore spaces, the material might also find application in some filtration systems, for either water or chemical processing. The mathematical descriptions derived by this group could facilitate the development of a variety of applications, the researchers say.

“This is an inspiring study on the mechanics of 3-D graphene assembly,” says Huajian Gao, a professor of engineering at Brown University, who was not involved in this work. “The combination of computational modeling with 3-D-printing-based experiments used in this paper is a powerful new approach in engineering research. It is impressive to see the scaling laws initially derived from nanoscale simulations resurface in macroscale experiments under the help of 3-D printing,” he says.

This work, Gao says, “shows a promising direction of bringing the strength of 2-D materials and the power of material architecture design together.”

There’s a video describing the work,

Here’s a link to and a citation for the paper,

The mechanics and design of a lightweight three-dimensional graphene assembly by Zhao Qin, Gang Seob Jung, Min Jeong Kang, and Markus J. Buehler. Science Advances  06 Jan 2017: Vol. 3, no. 1, e1601536 DOI: 10.1126/sciadv.1601536  04 January 2017

This paper appears to be open access.

Handling massive digital datasets the quantum way

A Jan. 25, 2016 news item on phys.org describes a new approach to analyzing and managing huge datasets,

From gene mapping to space exploration, humanity continues to generate ever-larger sets of data—far more information than people can actually process, manage, or understand.

Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at [Massachusetts Institute of Technology] MIT, the University of Waterloo, and the University of Southern California [USC}.

A Jan. 25, 2016 MIT news release (*also on EurekAlert*), which originated the news item, describes the theory in more detail,

… Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd [ explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

“That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

Here’s a link to and a citation for the paper,

Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone, & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138 Published 25 January 2016

This paper is open access.

ETA Jan. 25, 2016 1245 hours PST,

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

*’also on EurekAlert’ text and link added Jan. 26, 2016.

The geometry of baking with Alex Bellos and Evil Mad Scientist Laboratories

Alex Bellos has written some of my* favourite posts at the UK’s Guardian science blogs (for example, my Dec. 18,2012 posting about Bellos’ discussion of the math genius Ramanujan and my Oct. 17, 2012 posting about Bellos’ exploration of mathematics as a spiritual practice in Japan). Earlier this week in a June 26, 2013 posting Bellos wrote about a ‘new craze’, edible mathematics,  in a way that makes me wish I could revisit grade 10 geometry but with a good teacher this time (Note: Links have been removed),

When you slice a cone the surface produced is either a circle, an ellipse, a parabola or a hyperbola.

These curves are known as the conic sections.

And when you slice a scone in the shape of a cone, you get a sconic section – the latest craze in edible mathematics, a vibrant new culinary field.

On their fabulous website, the folk at Evil Mad Scientist provide a step-by-step guide to baking the sconic sections.

Here’s a sconic section image from the June 25, 2013 ‘Sconic sections’ posting by Lenore on the Evil Mad Scientist website,

To highlight the shapes even further, you can color in the cut surfaces with your favorite scone topping. Here, raspberry preserves show off a hyperbolic cut. [downloaded from http://www.evilmadscientist.com/2013/sconic-sections/]

To highlight the shapes even further, you can color in the cut surfaces with your favorite scone topping. Here, raspberry preserves show off a hyperbolic cut. [downloaded from http://www.evilmadscientist.com/2013/sconic-sections/]

Bellos highlights other edible mathematics projects including Maths on Toast and Dashing Bean but since I’ve always loved Escher I’m going to feature one of the other projects Bellos mentions, George Hart and his Möbius bagel,

After being cut, the two halves can be moved but are still linked together, each passing through the hole of the other.   (So when you buy your bagels, pick ones with the biggest holes.) [downloaded from http://georgehart.com/bagel/bagel.html]

After being cut, the two halves can be moved but are still linked together, each passing through
the hole of the other. (So when you buy your bagels, pick ones with the biggest holes.) [downloaded from http://georgehart.com/bagel/bagel.html]

Hart is serious about his Möbius bagel, from the bagel posting (Note: A link has been removed),

It is much more fun to put cream cheese on these bagels than on an ordinary bagel. In additional to
the intellectual stimulation, you get more cream cheese, because there is slightly more surface area.

Topology problem: Modify the cut so the cutting surface is a one-twist Mobius strip.
(You can still get cream cheese into the cut, but it doesn’t separate into two parts.)

Calculus problem: What is the ratio of the surface area of this linked cut
to the surface area of the usual planar bagel slice?

Note: I have had my students do this activity in my Computers and Sculpture class.  It is very successful if the students work in pairs, with two bagels per team.  For the first bagel, I have them draw the indicated lines with a “sharpie”.  Then they can do the second bagel without the lines. (We omit the schmear of cream cheese.) After doing this, one can better appreciate the stone carving of Keizo Ushio, who makes analogous cuts in granite to produce monumental sculpture.

Hope you enjoyed this mathematical ‘amuse-bouche’. If you want more, Bellos has included a few ‘how to’ videos, as well as, other images and links to websites in his posting.

* ‘my’ added to sentence on Aug. 12, 2015.